Speech recognition applications are commonly used in a variety of fields and systems which require conversion of voice or audio into text. While each industry, business and even each organization may use a unique professional vocabulary or jargon, characterized by special words, linguistic features, terms, idioms, and the like, applying a generic speech recognition model may result in low performance. One conspicuous problem that arises in speech recognition models is known as out-of-vocabulary words (OOV). Words such as innovative names of products, companies, trademarks, or words that are rare in generic contexts but are widely used in a specific domain may be absent from the training data of the generic model, and are hence not recognized. Furthermore, such words might be particularly central to a certain domain, hence failing to capture them significantly decreases model performance from the point of view of particular users. A process of adapting or “training” the generic model to a specific unique jargon is usually a long process that may require manual transcription of masses of data. There is a need for an automatic adaptation of a generic speech recognition model to a specific unique, jargon and for addressing the OOV problem.
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